December 3, 2012 | Given today’s unpredictable retail environment it is no longer enough to just “keep an eye” on inventory levels. All of us in the toy industry – manufacturer, retailer, licensor, etc. – are all trying to do the same thing in our planning process: estimate what and how much the end consumer will consume. Many methodologies exist for determining needs at the wholesale level but we believe – and have proven time and time again – that relying on POS data offers the least risky and most accurate way to do it.

In the ERS forecast software, we integrate multiple points of data including wholesale and store level inventory, wholesale production, store level POS sales, seasonality profiles, and perhaps most importantly – the user’s thinking – to provide the most accurate future sales predictions. Any forecast needs to take into consideration all the realities that occur at retail: changing door counts, new promotions, changes in pricing, change in sales pattern, etc. You need the ability to “teach” the forecast to automatically adjust for these events. The key is to list the events and then define thresholds or tolerances to each.

Here is a short list of some events and how you might adjust for them:

Change in sales trend
Monitor the last six weeks of sales and if actual sales are above or below what they should be, increase or decrease future sales by half of that trend (based on the sales curve.) For example, if the sales trend is 10% higher than planned, increase future weeks by 5%.

Change in store countIf stores are being added or subtracted, increase or decrease the future sales based on the average unit sales per store per week. If currently you sell on average 10 units per week per store and 100 stores are being added, add 1,000 units to the forecast. Over time, as you build POS history with the new stores, you can remove this adjustment.

Current store inventory positionSet thresholds for the minimum amount of inventory at store level for a SKU, then make adjustments for stores that are below the threshold. For example, if on average each store should have at least 6 units on hand of a particular SKU but 200 stores only have 4, add 400 units to bring them back up. Be sure to communicate this opportunity with the retail buyer or planner so there is an execution plan.

Building this type of logic into your forecasting regimen greatly improves your accuracy and greatly reduces outside spreadsheets and manual manipulation that are a drag on so many companies’ productivity levels. Once you are comfortable that you have the most accurate prediction of future sales, you can line in your inventory and use a simple open- to-buy calculation to determine needs.

However you manage your forecasting, we recommend you consider the impact of such “look-out points.” In fact, two of our most forward-thinking clients deemed the calculations of these variables so important that they urged us to accommodate this functionality in our software. We are pleased that this has resulted in forecasts that are more accurate and take less time to calculate.